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how to test face detector?

asked 2013-01-25 00:15:31 -0600

mrgloom gravatar image

updated 2013-01-25 05:20:53 -0600

HaarDetectObjects have many params, sometimes it fails to find face or detects not face.

  1. "The function scans the image several times at different scales" how many scales used?
  2. What images been used for training cascades in OpenCV?How rect size was selected?Only a face without a chin and hair?
  3. How to tune optimal params for my own test image set? For example I mark test image set by hand with rectangles, and then somehow test performance comparing detected rects.

Update: About training phase.

Training illumination varying faces in one detector resulted in pretty poor. The generated detector became sensitive to illumination rather than robust to illumination. This detector does not detect non-illuminated normal frontal faces. This makes sense because normal frontal faces did not exist in training sets so many. Training multi-view faces in one time resulted in the same thing.

So how to build generalized detector? Big face database is not enough, if we have large variation in data? What parameters should I choose if I don't want to miss any of faces but don't care about false positives?

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answered 2013-01-25 04:06:45 -0600

Ben gravatar image
  1. the amount of scales is defined by the scaleFactor parameter. Starting from the minimum size window, the window will be scaled by that factor until it reaches the maximum size. If, for example the minimum size is 50x50, the scale factor is 1.1 and the maximum size is 200x200, you would get 15 scaling steps (50, 55, 61, 66, 73, 80, 89, 97, 107, 118, 130, 143, 157, 173, 190) At least, that's how I think it works ;)

  2. There are different cascades available in OpenCV and they probably used different databases for training, but I can't tell you, which ones.

  3. Here you get a lot of informations on how to train and test haar cascades. I hope it helps.

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Asked: 2013-01-25 00:15:31 -0600

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Last updated: Jan 25 '13